Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

The efficacy of definitive concurrent or sequential chemoradiotherapy (dCRT) varies significantly among limited-stage small-cell lung cancer (LS-SCLC) patients, with about 10%–13% of patients achieving 5-year survival, while 58% of patients die within 1 year. 1–3 Therefore, there is an urgent need to find biomarkers for early prediction of the efficacy of dCRT in LS-SCLC in support of risk stratification. Tumourigenesis and progression are heterogeneous at the phenotypic, physiologic and genomic levels, making predictive information obtained via radiomic or genomic profiling alone of limited value for clinical decision making. 4 - 6 The present study aimed to develop a combination of genomic, radiomic and fused radiogenomic biomarkers for predicting the response of LS-SCLC to dCRT in training and validation cohorts

tivariate Cox analyses.The correlation between Rad-score and PFS was significant in the training cohort (mPFS, 14.83 vs. 10.63 months, p = .006;hazard ratio = 2.152, 95% confidence interval: 1.236−3.749,p = .007),as shown in Figure 2 and Table S3.The C-index for the ability of the Rad-score to predict PFS in the training set was .574,and the area under the curve (AUC) values for prediction of 6-and 12-month PFS were .583and .601,respectively (Figure 3 and Table S4).A significant association between Rad-score and PFS was also demonstrated in the validation cohort (mPFS, 14.20 vs. 7.83 months, p = .015;Cindex = .656;AUC for 6-and 12-month PFS: .746and .640,respectively).
We previously identified novel biomarkers of alterations in the CDK4, GATA6 and MAPK/ERK pathway genes as well as tumour mutational burden (TMB) status as predictors of the response to dCRT in a large cohort of LS-SCLC patients. 9According to the prior genomic model (Genes-score pr ) combined by these four features, patients with low Genes-score pr (no gene mutations and high TMB) showed significant improved PFS in training and validation cohorts (mPFS, low Genes-score pr vs. high Genes-score pr , 18.43 vs. 11.13months, p < .001;mPFS, low Genes-score pr vs. high Genes-score pr , 9.27 vs. 5.8 months, p = .014)(Figure 2).
And, posterior genomic biomarkers (Genes-score po ) of CDK4 and TMB status were recognised as significant factors (Table S3) according to the univariate and multivariate Cox analyses.In the training group, patients with a low Genes-score po (no CDK4 amplification and high TMB) showed significantly prolonged PFS compared with patients with high Gene-score po (CDK4 amplification and/or low TMB) (mPFS, 16.03 vs. 9.03 months, p = .006)(Figure 2).In the validation set, Kaplan-Meier analysis showed that the Genes-score po model could effectively distinguish SCLC patients with different PFS durations (mPFS, low Genes-score po vs. high Genes-score po , 17.77 vs. 9.27 months, p = .001)(Figure 2).
As shown in Figure 3 and Table S4, the corresponding combination of radiogenomic models (Rad-Genes pr and Rad-Genes po ) all demonstrated higher C-index and 6-and 12-month AUC values of the ability to predict PFS than individual radiomic (Rad-score) or genomic models (Genes-score pr and Genes-score po ), respectively.
To the best of our knowledge, no research has been conducted to date to determine the ability of fused radiogenomic features to predict the efficacy of dCRT in LS-SCLC. 10 According to the Rad-score, Genes-score pr and Genes-score po , Kaplan-Meier analyses were conducted according to the combination signature (Rad-Genes pr/po ) built from the radiogenomic factors (Figure 2).Significant associations (log-rank p < .05)were found between Rad-Genes po and PFS in the training and validation subgroups.The mPFS durations for the high-risk, intermediate-risk and low-risk groups were 6.70, 12.17 and 16.10 months, respectively, in the training cohort and 6.7, 10.5 and 17.77 months, respectively, in the validation cohort.However, Rad-Genes pr model was only associated with PFS in the training cohort (mPFS, 17.77 vs. 13.07 vs. 7.67 months, p < .001),there was no statistical difference in the validation cohort (mPFS, 9.27 vs. 5.87 vs. 5.4 months, p = .121).
Overall, we identified several radiomic, genomic and radiogenomic biomarkers with the potential to identify LS-SCLC patients with reduced risk of progression after dCRT, and a combination of radiogenomic features was found to form the optimal prediction model based on the higher C-index and AUC values compared with individual radiomic and genomic models.Given that the Rad-Genes pr model failed to show a survival difference in the validation cohort, Genes-score po developed by CDK4 and TMB maybe better genomic models.The radiogenomic model combining the Rad-score model, CDK4 amplification and TMB status could successfully stratify patients into highrisk, intermediate-risk and low-risk groups, and thus, may be conducive for screening SCLC patients according to the likelihood of improved PFS.As our research was conducted by retrospective, single centre and relatively small sample size of patients, which may limit the generalisability of the results.And the combined radiogenomic predictive model established in this study requires external validation with a larger sample size of data collected from more medical centres.

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Clin.Transl.Med.2024;14:e1522.wileyonlinelibrary.com/journal/ctm2 1 of 4 https://doi.org/10.1002/ctm2.1522F I G U R E 1 Selection of radiomic features related to PFS using LASSO regression.(A) Cross-validation curve.(B) Coefficients curves for radiomic features.MSE, mean square error.F I G U R E 2 Survival analyses of Rad-score (A and F), Genes-score pr (B and G), Rad-Genes pr (C and H), Genes-score po (D and I) and Rad-Genes po (E and G) in the training and validation cohorts.F I G U R E 3 The receiver operating characteristic (ROC) curve analyses of Rad-score (A and F), Genes-score pr (B and G), Rad-Genes pr (C and H), Genes-score po (D and I) and Rad-Genes po (E and G) in the training and validation cohorts.
Li Li designed this study.Li Li and Ying Yin acquired clinical data and performed patient follow-ups.Li Li, Jinghao Duan, Yongsheng Gao and Fengchang Yang performed data analysis.Li Li, Wenjie Tang, Xiaoyu Song, Jinfeng Cui and Tao Hu edited the manuscript.Jinming Yu and Shuanghu Yuan conceived and supervised the study.A C K N O W L E D G E M E N T SWe would like to thank all the patients and family members who gave their consent for use of their data in this study.This study was supported in part by the National Natural Science Foundation of China (NSFC82073345), the Natural Science Foundation of Shandong Province Innovation and Development Joint Fund (ZR202209010002), the Taishan Scholars Program and Jinan Clinical Medicine Science and Technology Innovation Plan (202019060) to Shuanghu Yuan and the Major Basic Research Program of the National Natural Science Foundation of Shandong(ZR2022ZD16), Natural Science Youth Foundation of Shandong (ZR2023QH155), and Postdoctoral Science Foundation of China (2023M742159) to Li Li.